Translation and analysis of words by ChatGPT artificial intelligence
On this page you can get a detailed analysis of a word or phrase, produced by the best artificial intelligence technology to date:
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- usage examples (several phrases with translation)
- etymology
What (who) is stochastic walk - definition
![Wiener]] or [[Brownian motion]] process on the surface of a sphere. The Wiener process is widely considered the most studied and central stochastic process in probability theory.<ref name="doob1953stochasticP46to47"/><ref name="RogersWilliams2000page1"/><ref name="Steele2012page29"/>](https://commons.wikimedia.org/wiki/Special:FilePath/BMonSphere.jpg?width=200 "Wiener]] or [[Brownian motion]] process on the surface of a sphere. The Wiener process is widely considered the most studied and central stochastic process in probability theory.<ref name=\"doob1953stochasticP46to47\"/><ref name=\"RogersWilliams2000page1\"/><ref name=\"Steele2012page29\"/>")
![Mathematician [[Joseph Doob]] did early work on the theory of stochastic processes, making fundamental contributions, particularly in the theory of martingales.<ref name="Getoor2009"/><ref name="Snell2005"/> His book ''Stochastic Processes'' is considered highly influential in the field of probability theory.<ref name="Bingham2005"/>](https://commons.wikimedia.org/wiki/Special:FilePath/Joseph Doob.jpg?width=200 "Mathematician [[Joseph Doob]] did early work on the theory of stochastic processes, making fundamental contributions, particularly in the theory of martingales.<ref name=\"Getoor2009\"/><ref name=\"Snell2005\"/> His book ''Stochastic Processes'' is considered highly influential in the field of probability theory.<ref name=\"Bingham2005\"/>")
Wikipedia
- This page is concerned with the stochastic modelling as applied to the insurance industry. For other stochastic modelling applications, please see Monte Carlo method and Stochastic asset models. For mathematical definition, please see Stochastic process.
"Stochastic" means being or having a random variable. A stochastic model is a tool for estimating probability distributions of potential outcomes by allowing for random variation in one or more inputs over time. The random variation is usually based on fluctuations observed in historical data for a selected period using standard time-series techniques. Distributions of potential outcomes are derived from a large number of simulations (stochastic projections) which reflect the random variation in the input(s).
Its application initially started in physics. It is now being applied in engineering, life sciences, social sciences, and finance. See also Economic capital.
